Add parallel keyword to GMM (#114)

src/train.jl

@@ -28,19 +28,19 @@ GMM(x::Vector{T}) where T <: AbstractFloat = GMM(reshape(x, length(x), 1))  # st
 
 ## constructors based on data or matrix
 function GMM(n::Int, x::DataOrMatrix{T}; method::Symbol=:kmeans, kind=:diag,
-             nInit::Int=50, nIter::Int=10, nFinal::Int=nIter, sparse=0) where T <: AbstractFloat
+             nInit::Int=50, nIter::Int=10, nFinal::Int=nIter, sparse=0, parallel::Bool=true) where T <: AbstractFloat
     if n < 2
         return GMM(x, kind=kind)
     elseif method == :split
-        return GMM2(n, x, kind=kind, nIter=nIter, nFinal=nFinal, sparse=sparse)
+        return GMM2(n, x, kind=kind, nIter=nIter, nFinal=nFinal, sparse=sparse, parallel=parallel)
     elseif method == :kmeans
-        return GMMk(n, x, kind=kind, nInit=nInit, nIter=nIter, sparse=sparse)
+        return GMMk(n, x, kind=kind, nInit=nInit, nIter=nIter, sparse=sparse, parallel=parallel)
     else
         error("Unknown method ", method)
     end
 end
 ## a 1-dimensional Gaussian can be initialized with a vector, skip kind=
-GMM(n::Int, x::Vector{T}; method::Symbol=:kmeans, nInit::Int=50, nIter::Int=10, nFinal::Int=nIter, sparse=0) where T <: AbstractFloat = GMM(n, reshape(x, length(x), 1); method=method, kind=:diag, nInit=nInit, nIter=nIter, nFinal=nFinal, sparse=sparse)
+GMM(n::Int, x::Vector{T}; method::Symbol=:kmeans, nInit::Int=50, nIter::Int=10, nFinal::Int=nIter, sparse=0, parallel::Bool=true) where T <: AbstractFloat = GMM(n, reshape(x, length(x), 1); method=method, kind=:diag, nInit=nInit, nIter=nIter, nFinal=nFinal, sparse=sparse, parallel=parallel)
 
 ## we sometimes end up with pathological gmms
 function sanitycheck!(gmm::GMM)
@@ -73,7 +73,7 @@ end
 
 
 ## initialize GMM using Clustering.kmeans (which uses a method similar to kmeans++)
-function GMMk(n::Int, x::DataOrMatrix{T}; kind=:diag, nInit::Int=50, nIter::Int=10, sparse=0) where T <: AbstractFloat
+function GMMk(n::Int, x::DataOrMatrix{T}; kind=:diag, nInit::Int=50, nIter::Int=10, sparse=0, parallel::Bool=true) where T <: AbstractFloat
     nₓ, d = size(x)
     hist = [History(@sprintf("Initializing GMM, %d Gaussians %s covariance %d dimensions using %d data points", n, diag, d, nₓ))]
     @info(last(hist).s)
@@ -141,22 +141,22 @@ function GMMk(n::Int, x::DataOrMatrix{T}; kind=:diag, nInit::Int=50, nIter::Int=
     @info(last(hist).s)
     gmm = GMM(w, μ, Σ, hist, nxx)
     sanitycheck!(gmm)
-    em!(gmm, x; nIter=nIter, sparse=sparse)
+    em!(gmm, x; nIter=nIter, sparse=sparse, parallel=parallel)
     return gmm
 end
 
 ## Train a GMM by consecutively splitting all means.  n most be a power of 2
 ## This kind of initialization is deterministic, but doesn't work particularily well, its seems
 ## We start with one Gaussian, and consecutively split.
-function GMM2(n::Int, x::DataOrMatrix; kind=:diag, nIter::Int=10, nFinal::Int=nIter, sparse=0)
+function GMM2(n::Int, x::DataOrMatrix; kind=:diag, nIter::Int=10, nFinal::Int=nIter, sparse=0, parallel::Bool=true)
     log2n = round(Int,log2(n))
     2^log2n == n || error("n must be power of 2")
     gmm = GMM(x, kind=kind)
     tll = [avll(gmm, x)]
     @info("0: avll = ", tll[1])
     for i in 1:log2n
         gmm = gmmsplit(gmm)
-        avll = em!(gmm, x; nIter=(i==log2n ? nFinal : nIter), sparse=sparse)
+        avll = em!(gmm, x; nIter=(i==log2n ? nFinal : nIter), sparse=sparse, parallel=parallel)
         @info(i, avll)
         append!(tll, avll)
     end
@@ -235,7 +235,7 @@ end
 # the log-likelihood history, per data frame per dimension
 ## Note: 0 iterations is allowed, this just computes the average log likelihood
 ## of the data and stores this in the history.
-function em!(gmm::GMM, x::DataOrMatrix; nIter::Int = 10, varfloor::Float64=1e-3, sparse=0, debug=1)
+function em!(gmm::GMM, x::DataOrMatrix; nIter::Int = 10, varfloor::Float64=1e-3, sparse=0, parallel::Bool=true, debug=1)
     size(x,2)==gmm.d || error("Inconsistent size gmm and x")
     d = gmm.d                   # dim
     ng = gmm.n                  # n gaussians
@@ -247,7 +247,7 @@ function em!(gmm::GMM, x::DataOrMatrix; nIter::Int = 10, varfloor::Float64=1e-3,
 
     for i in 1:nIter
         ## E-step
-        nₓ, ll[i], N, F, S = stats(gmm, x, parallel=true)
+        nₓ, ll[i], N, F, S = stats(gmm, x, parallel=parallel)
         ## M-step
         gmm.w = N / nₓ
         gmm.μ = F ./ N